Evidence map›Paper›PMID 23240691›Full record

ArticleJournal of chemical information and modeling2013

FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach.

Hongyi Zhou, Jeffrey Skolnick

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

  1. Article
  2. Article
  3. FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.Protein science : a publication of the Protein Society · 2024
    Article
  4. Article
  5. Article
  6. Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  7. Article
  8. FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.Journal of chemical information and modeling · 2021
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. FINDSITEJournal of chemical information and modeling · 2018
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hongyi ZhouCenter for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street, N.W., Atlanta, Georgia 30318, USA.
Jeffrey Skolnick

Funding

MARC STARS AT UVIT34GM008422 · NIGMS · UNIVERSITY OF THE VIRGIN ISLANDS · PI STOLZ, ROBERT · 1992 to 2021
$5.3M
Interaction Pattern Based Predictor of Protein StruturesR01GM048835 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI SKOLNICK, JEFFREY · 1994 to 2015
$4.0M
THEORY OF FOLDING TRANSITIONS IN A-HELICAL PROTEINSR01GM037408 · NIGMS · WASHINGTON UNIVERSITY · PI SKOLNICK, JEFFREY · 1987 to 2014
$3.9M
NIGMS NIH HHS GM-08422NIGMS NIH HHS GM-37408NIGMS NIH HHS GM-48835NIGMS NIH HHS R01 GM037408NIGMS NIH HHS R01 GM048835NIGMS NIH HHS T34 GM008422
6 · The paper itself

Abstract

Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITE(filt), that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITE(filt) with FINDSITE(X) that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITE(comb), is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITE(comb) is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITE(comb) gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITE(comb) makes the screening of millions of compounds across entire proteomes feasible. The FINDSITE(comb) web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html.

Indexed as

AlgorithmsUser-Computer InterfaceBinding SitesDatabases, ProteinDrug Evaluation, PreclinicalHumansLigandsModels, MolecularMolecular Docking SimulationProtein ConformationProteomicsReceptors, G-Protein-CoupledLigandsReceptors, G-Protein-Coupled

Identifiers

PMID23240691
PMCPMC3557555

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.